Nonlinear Least Squares Lattice Algorithm for Identifying the Power Amplifier with Memory Effects

The memory polynomial model (MPM) proposed recently, is shown to be a good model to capture the memory nonlinear effects in the power amplifier (PA). When extracting the model coefficients, the memory length of the PA has to be predefined, while it is actually unknown previously. In this paper, the...

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Veröffentlicht in:2006 IEEE 63rd Vehicular Technology Conference Jg. 5; S. 2149 - 2153
Hauptverfasser: Hui Li, Zhaowu Chen, Desheng Wang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2006
Schlagworte:
Baseband
Computational complexity
Computational modeling
Filtering algorithms
Lattices
Least squares methods
memory polynomial model
nonlinear least squares lattice algorithm
Polynomials
power amplifier with memory effects
Power amplifiers
Power system modeling
Resonance light scattering
ISBN:9780780393912, 0780393910
ISSN:1550-2252
Online-Zugang:Volltext
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Abstract The memory polynomial model (MPM) proposed recently, is shown to be a good model to capture the memory nonlinear effects in the power amplifier (PA). When extracting the model coefficients, the memory length of the PA has to be predefined, while it is actually unknown previously. In this paper, the adaptive nonlinear least squares lattice algorithm is employed to identify the PA with memory effects based on MPM. Making use of the order-recursive behavior of the algorithm, the MPM with the optimum memory length is obtained. The computational complexity of the identification algorithm is equivalent to the recursive-least-squares (RLS) algorithm. And the same model accuracy as acquired by the RLS algorithm can be achieved. Simulation results show the fast convergence and numerical stability of the proposed approach
AbstractList The memory polynomial model (MPM) proposed recently, is shown to be a good model to capture the memory nonlinear effects in the power amplifier (PA). When extracting the model coefficients, the memory length of the PA has to be predefined, while it is actually unknown previously. In this paper, the adaptive nonlinear least squares lattice algorithm is employed to identify the PA with memory effects based on MPM. Making use of the order-recursive behavior of the algorithm, the MPM with the optimum memory length is obtained. The computational complexity of the identification algorithm is equivalent to the recursive-least-squares (RLS) algorithm. And the same model accuracy as acquired by the RLS algorithm can be achieved. Simulation results show the fast convergence and numerical stability of the proposed approach
Author Zhaowu Chen
Hui Li
Desheng Wang
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  organization: Dept. of Electron. Eng., Tsinghua Univ., Beijing
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  surname: Zhaowu Chen
  fullname: Zhaowu Chen
  organization: Dept. of Electron. Eng., Tsinghua Univ., Beijing
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  surname: Desheng Wang
  fullname: Desheng Wang
  organization: Dept. of Electron. Eng., Tsinghua Univ., Beijing
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Snippet The memory polynomial model (MPM) proposed recently, is shown to be a good model to capture the memory nonlinear effects in the power amplifier (PA). When...
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StartPage 2149
SubjectTerms Baseband
Computational complexity
Computational modeling
Filtering algorithms
Lattices
Least squares methods
memory polynomial model
nonlinear least squares lattice algorithm
Polynomials
power amplifier with memory effects
Power amplifiers
Power system modeling
Resonance light scattering
Title Nonlinear Least Squares Lattice Algorithm for Identifying the Power Amplifier with Memory Effects
URI https://ieeexplore.ieee.org/document/1683236
Volume 5
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